A discrete animal migration algorithm for dual-resource constrained energy-saving flexible job shop scheduling problem

This paper presents a discrete animal migration optimization (DAMO) to solve the dual-resource constrained energy-saving flexible job shop scheduling problem (DRCESFJSP), with the aim of minimizing the total energy consumption in the workshop. A job-resource-based two-vector encoding method is designed to represent the scheduling solution, and an energy-saving decoding approach is given based on the left-shift rule. To ensure the quality and diversity of initial scheduling solutions, a heuristic approach is employed for the resource assignment, and some dispatching rules are applied to acquire the operation permutation. In the proposed DAMO, based on the characteristics of the DRCESFJSP problem, the search operators of the basic AMO are discretized to adapt to the problem under study. An animal migration operator is presented based on six problem-based neighborhood structures, which dynamically changes the search scale of each animal according to its solution quality. An individual updating operator based on crossover operation is designed to obtain new individuals through the crossover operation between the current individual and the best individual or a random individual. To evaluate the performance of the proposed algorithm, the Taguchi design of experiment method is first applied to obtain the best combination of parameters. Numerical experiments are carried out based on 32 instances in the existing literature. Computational data and statistical comparisons indicate that both the left-shift decoding rule and population initialization strategy are effective in enhancing the quality of the scheduling solutions. It also demonstrate that the proposed DAMO has advantages against other compared algorithms in terms of the solving accuracy for solving the DRCESFJSP.

An Early Breast Cancer Detection System Using Recurrent Neural Network (RNN) with Animal Migration Optimization (AMO) Based Classification Method

Breast cancer can be detected using early signs of it mammograms and digital mammography. For Computer Aided Detection (CAD), algorithms can be developed using this opportunities. Early detection is assisted by self-test and periodical check-ups and it can enhance the survival chance significantly. Due the need of breast cancer’s early detection and false diagnosis impact on patients, made researchers to investigate Deep Learning (DL) techniques for mammograms. So, it requires a non-invasive cancer detection system, which is highly effective, accurate, fast as well as robust. Proposed work has three steps, (i) Pre-processing, (ii) Segmentation, and (iii) Classification. Firstly, preprocessing stage removing noise from images by using mean and median filtering algorithms are used, while keeping its features intact for better understanding and recognition, then edge detection by using canny edge detector. It uses Gaussian filter for smoothening image. Gaussian smoothening is used for enhancing image analysis process quality, result in blurring of fine-scaled image edges. In the next stage, image representation is changed into something, which makes analyses process as a simple one. Foreground and background subtraction is used for accurate breast image detection in segmentation. After completion of segmentation stage, the remove unwanted image in input image dataset. Finally, a novel RNN forclassifying and detecting breast cancer using Auto Encoder (AE) based RNN for feature extraction by integrating Animal Migration Optimization (AMO) for tuning the parameters of RNN model, then softmax classifier use RNN algorithm. Experimental results are conducted using Mini-Mammographic (MIAS) dataset of breast cancer. The classifiers are measured through measures like precision, recall, f-measure and accuracy.

Sustainable road-kill mitigation in Gladak Perak Bridge at Lumajang, Indonesia

Traffic accidents involving animals occur every year. Roadkill is a serious problem faced by the whole world, including Indonesia. Therefore, it is necessary to modify road accessories to prevent accidents, both from animals and road users. Prevention can be done in several ways, such as by installing fences or creating crossing paths, for animals. The fence can be used as a barrier between the driving lane and the animal path, where they can carry out activities such as playing without disturbing road users. Meanwhile, the making of crossing paths can be used by animals as access for animal migration. This study would like to propose a design for implementing cross-fencing mitigation at Gladak Perak Bridge at Lumajang, Indonesia. This location is an accident-prone area due to the sudden crossing of monkeys, which has been a myth in the community. Through the implementation of the installation of road dividers, it is hoped that the road design at the research site becomes wildlife friendly road and the management of traffic also meet the Indonesian design standards for inter-city roads without reducing tourism potential.

Animal Migration: An Overview of One of Nature's Great Spectacles

The twenty-first century has witnessed an explosion in research on animal migration, in large part due to a technological revolution in tracking and remote-sensing technologies, along with advances in genomics and integrative biology. We now have access to unprecedented amounts of data on when, where, and how animals migrate across various continents and oceans. Among the important advancements, recent studies have uncovered a surprising level of variation in migratory trajectories at the species and population levels with implications for both speciation and the conservation of migratory populations. At the organismal level, studies linking molecular and physiological mechanisms to traits that support migration have revealed a remarkable amount of seasonal flexibility in many migratory animals. Advancements in the theory for why animals migrate have resulted in promising new directions for empirical studies. We provide an overview of the current state of knowledge and promising future avenues of study. Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 52 is November 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Fusion of wildlife tracking and satellite geomagnetic data for the study of animal migration

Background Migratory animals use information from the Earth’s magnetic field on their journeys. Geomagnetic navigation has been observed across many taxa, but how animals use geomagnetic information to find their way is still relatively unknown. Most migration studies use a static representation of geomagnetic field and do not consider its temporal variation. However, short-term temporal perturbations may affect how animals respond - to understand this phenomenon, we need to obtain fine resolution accurate geomagnetic measurements at the location and time of the animal. Satellite geomagnetic measurements provide a potential to create such accurate measurements, yet have not been used yet for exploration of animal migration. Methods We develop a new tool for data fusion of satellite geomagnetic data (from the European Space Agency’s Swarm constellation) with animal tracking data using a spatio-temporal interpolation approach. We assess accuracy of the fusion through a comparison with calibrated terrestrial measurements from the International Real-time Magnetic Observatory Network (INTERMAGNET). We fit a generalized linear model (GLM) to assess how the absolute error of annotated geomagnetic intensity varies with interpolation parameters and with the local geomagnetic disturbance. Results We find that the average absolute error of intensity is − 21.6 nT (95% CI [− 22.26555, − 20.96664]), which is at the lower range of the intensity that animals can sense. The main predictor of error is the level of geomagnetic disturbance, given by the Kp index (indicating the presence of a geomagnetic storm). Since storm level disturbances are rare, this means that our tool is suitable for studies of animal geomagnetic navigation. Caution should be taken with data obtained during geomagnetically disturbed days due to rapid and localised changes of the field which may not be adequately captured. Conclusions By using our new tool, ecologists will be able to, for the first time, access accurate real-time satellite geomagnetic data at the location and time of each tracked animal, without having to start new tracking studies with specialised magnetic sensors. This opens a new and exciting possibility for large multi-species studies that will search for general migratory responses to geomagnetic cues. The tool therefore has a potential to uncover new knowledge about geomagnetic navigation and help resolve long-standing debates.

Linking animal migration and ecosystem processes: data-driven simulation of propagule dispersal by migratory herbivores

Animal migration is a key process underlying active subsidies and species dispersal over long distances, which affects the connectivity and functioning of ecosystems. Despite much research describing patterns of where animals migrate, we still lack a framework for quantifying and predicting how animal migration affects ecosystem processes. In this study, we aim to integrate animal movement behavior and ecosystem functioning by developing a predictive modeling framework that can inform ecosystem management and conservation. Our framework models individual-level migration trajectories between populations' seasonal ranges as well as the resulting dispersal and fate of propagules carried by the migratory animals, and it can be calibrated using empirical data at every step of the modeling process. As a case study, we applied our framework to model the spread of guava seeds, Psidium guajava, by a population of migratory Galapagos tortoises, Chelonoidis porteri, across Santa Cruz Island. Galapagos tortoises are large herbivores that transport seeds and nutrients across the island, while Guava is one of the most problematic invasive species in the Galapagos archipelago. Our model is able to predict the pattern of spread of guava seeds alongside tortoises' downslope migration range, and it identified areas most likely to see germination success and establishment. Our results show that Galapagos tortoises' seed dispersal may particularly contribute to guava range expansion on Santa Cruz Island, due to both long gut retention time and tortoise's long-distance migration across vegetation zones. In particular, we predict that tortoises are dispersing a significant amount of guava seeds into the Galapagos National Park, which has important consequences for the native flora. The flexibility and modularity of our framework allows for the integration of multiple data sources. It also allows for a wide range of applications to investigate how migratory animals affect ecosystem processes, including propagule dispersal but also other processes such as nutrient transport across ecosystems. Our framework is also a valuable tool for predicting how animal-mediated propagule dispersal can be affected by environmental change. These different applications can have important conservation implications for the management of ecosystems that include migratory animals.